Scalable and accurate multi-GPU based image reconstruction of large-scale ptychography data

TL;DR

This paper introduces PtyGer, a multi-GPU tool that significantly improves large-scale ptychographic image reconstruction efficiency within a single node by optimizing GPU communication and parallelization.

Contribution

The paper presents a novel hybrid parallelization model and implements PtyGer, enabling scalable, accurate, and efficient multi-GPU ptychographic reconstruction on intra-node systems.

Findings

PtyGer achieves high intra-node GPU scalability.

The hybrid parallelization model improves performance over MPI-only approaches.

The reconstruction accuracy is preserved with the new method.

Abstract

While the advances in synchrotron light sources, together with the development of focusing optics and detectors, allow nanoscale ptychographic imaging of materials and biological specimens, the corresponding experiments can yield terabyte-scale large volumes of data that can impose a heavy burden on the computing platform. While Graphical Processing Units (GPUs) provide high performance for such large-scale ptychography datasets, a single GPU is typically insufficient for analysis and reconstruction. Several existing works have considered leveraging multiple GPUs to accelerate the ptychographic reconstruction. However, they utilize only Message Passing Interface (MPI) to handle the communications between GPUs. It poses inefficiency for the configuration that has multiple GPUs in a single node, especially while processing a single large projection, since it provides no optimizations to handle the heterogeneous GPU interconnections containing both low-speed links, e.g., PCIe, and high-speed links, e.g., NVLink. In this paper, we provide a multi-GPU implementation that can effectively solve large-scale ptychographic reconstruction problem with optimized performance on intra-node multi-GPU. We focus on the conventional maximum-likelihood reconstruction problem using conjugate-gradient (CG) for the solution and propose a novel hybrid parallelization model to address the performance bottlenecks in CG solver. Accordingly, we develop a tool called PtyGer (Ptychographic GPU(multiple)-based reconstruction), implementing our hybrid parallelization model design. The comprehensive evaluation verifies that PtyGer can fully preserve the original algorithm's accuracy while achieving outstanding intra-node GPU scalability.